In which processes can AI create real value?
According to the Bank for International Settlements (BIS), banks are already using AI in areas such as fraud detection, creditworthiness assessment, compliance process automation, customer service and risk management.
This shift is important.
Rather than using a single model for every need, financial institutions are looking for more focused solutions, trained or adapted for specific processes.
Why Specialization May Be More Valuable
Finance is an industry where mistakes are costly.
A model that is good at writing text may be useful for customer communications, but that does not automatically mean it is suitable for analyzing foreign-exchange exposure, forecasting liquidity needs or assessing credit risk.
These processes require specific data, clear rules and strong controls.
This is why banks have a strong reason to develop AI specialized for specific tasks, rather than relying solely on general-purpose models.
One system can analyze thousands of transactions to identify unusual behavior. Another can help credit analysts assess financial information. Another can process regulatory documents or assist with risk monitoring.
The value does not necessarily lie in replacing people.
Often, what matters most is making specialists faster and more accurate.
From Foreign Exchange to Liquidity
One of the areas where specialization is particularly important is financial markets.
Banks manage foreign-exchange exposures, liquidity, interest rates and a large number of transactions every day. These processes generate vast amounts of data that can be analyzed by AI systems.
In foreign-exchange markets, for example, AI can be used to analyze patterns in data, identify unusual changes and support specialist decision-making.
In liquidity management, technology can help banks understand incoming and outgoing fund flows more quickly and identify potential pressures.
This becomes even more important in a financial system where capital movements and cross-border exposures are increasing. BIS reported that cross-border bank credit increased by 11% year-on-year in the first quarter of 2026, reaching approximately $39.5 trillion.
The more complex the financial system becomes, the greater the need for tools capable of processing information in real time.
But AI Does Not Eliminate Risk
Precisely because AI adoption is expanding, banks and regulators are increasingly focusing on risk controls.
An AI system can be highly useful, but it can also produce inaccurate results, rely on inappropriate data or create new technological dependencies.
The BIS has warned that the use of AI in the financial system may bring not only efficiency and innovation benefits, but also operational weaknesses, cyber risks and interconnected dependencies between institutions.
This is why the future model of banking will not simply be “more AI.”
It will be AI with controls, reliable data, human oversight and clearly defined processes.
The Bank of the Future May Have More AI, but Not Fewer People
Ironically, the specialization of AI may make the role of the financial expert even more important.
A system may identify a pattern or an anomaly, but someone still needs to understand the context, assess the risk and take responsibility for the decision.
This is particularly important in finance, where decisions can directly affect customers’ savings, loans, investments and financial stability.
Therefore, the transformation taking place in banks should not be viewed solely as a story of automation.
It is a story of reorganizing how people and technology make decisions together.
If general-purpose AI was the first phase of the revolution, the next phase may be much more practical: smaller, more specialized models integrated directly into financial processes.
Source: BIS • Reuters
FINARCH RECOMMENDS
Technology can make finance faster. But speed is not the same as decision quality.
The use of AI in banking can bring faster services, more advanced analysis and more efficient processes. But customers should understand that a financial decision should not rely solely on an algorithm or an automated recommendation.
Especially when it comes to loans, investments, savings or wealth management, data, transparency, risk and the customer’s financial objective matter.
AI can support analysis. Human expertise remains essential for interpretation and decision-making.
Our recommendation:
Use technology to access more information, not to give up informed decision-making.
The future of finance may be more digital. But a good financial decision still requires understanding before deciding.
This material is for informational purposes only and does not constitute individual investment advice.